Python- Master Machine Learning with Python- 3-in-1

Python: Master Machine Learning with Python: 3-in-1, available at $19.99, has an average rating of 3.4, with 144 lectures, based on 5 reviews, and has 88 subscribers.

You will learn about Evaluate and apply the most effective models to problems Deploy machine learning models using third-party APIs Interact with text data and build models to analyze it Use deep neural networks to build an optical character recognition system Work with image data and build systems for image recognition and biometric face recognition Eliminate common data wrangling problems in Pandas and scikit-learn as well as solve prediction visualization issues with Matplotlib Explore data visualization techniques to interact with your data in diverse ways This course is ideal for individuals who are Developers and data scientist, who have a basic machine learning knowledge and want to explore the various arenas of machine learning by creating insightful and interesting projects. or Python programmers who are looking to use machine-learning algorithms to create real-world applications. It is particularly useful for Developers and data scientist, who have a basic machine learning knowledge and want to explore the various arenas of machine learning by creating insightful and interesting projects. or Python programmers who are looking to use machine-learning algorithms to create real-world applications.

Enroll now: Python: Master Machine Learning with Python: 3-in-1

Summary

Title: Python: Master Machine Learning with Python: 3-in-1

Price: $19.99

Average Rating: 3.4

Number of Lectures: 144

Number of Published Lectures: 144

Number of Curriculum Items: 144

Number of Published Curriculum Objects: 144

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Evaluate and apply the most effective models to problems

Deploy machine learning models using third-party APIs

Interact with text data and build models to analyze it

Use deep neural networks to build an optical character recognition system

Work with image data and build systems for image recognition and biometric face recognition

Eliminate common data wrangling problems in Pandas and scikit-learn as well as solve prediction visualization issues with Matplotlib

Explore data visualization techniques to interact with your data in diverse ways

Who Should Attend

Developers and data scientist, who have a basic machine learning knowledge and want to explore the various arenas of machine learning by creating insightful and interesting projects.

Python programmers who are looking to use machine-learning algorithms to create real-world applications.

Target Audiences

Developers and data scientist, who have a basic machine learning knowledge and want to explore the various arenas of machine learning by creating insightful and interesting projects.

Python programmers who are looking to use machine-learning algorithms to create real-world applications.

You are a data scientist. Every day, you stare at reams of data trying to apply the latest and brightest of models to uncover new insights, but there seems to be an endless supply of obstacles. Your colleagues depend on you to monetize your firm’s data – and the clock is ticking. What do you do?

Troubleshooting Python Machine Learning is the answer.

Machine learning gives you powerful insights into data. Today, implementations of machine learning are adopted throughout Industry and its concepts are many. Machine learning is pervasive in the modern data-driven world. Used across many fields such as search engines, robotics, self-driving cars, and more.

The effective blend of Machine Learning with Python, helps in implementing solutions to real-world problems as well as automating analytical model.

This comprehensive 3-in-1 course is a comprehensive, practical tutorial that helps you get superb insights from your data in different scenarios and deploy machine learning models with ease. Explore the power of Python and create your own machine learning models with this project-based tutorial. Try and test solutions to solve common problems, while implementing Machine learning with Python.

Contents and Overview

This training program includes 3 complete courses, carefully chosen to give you the most comprehensive training possible.

The first course, Python Machine Learning Projects, covers Machine Learning with Python’s insightful projects.This video is a unique blend of projects that teach you what Machine Learning is all about and how you can implement machine learning concepts in practice. Six different independent projects will help you master machine learning in Python. The video will cover concepts such as classification, regression, clustering, and more, all the while working with different kinds of databases. You’ll be able to implement your own machine learning models after taking this course.

The second course, Python Machine Learning Solutions, covers 100 videos that teach you how to perform various machine learning tasks in the real world. Explore a range of real-life scenarios where machine learning can be used, and look at various building blocks. Throughout the course, you’ll use a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms. Discover how to deal with various types of data and explore the differences between machine learning paradigms such as supervised and unsupervised learning

The third course, Troubleshooting Python Machine Learning, covers practical and unique solutions to common Machine Learning problems that you face. Debug your models and research pipelines, so you can focus on pitching new ideas and not fixing old bugs.

By the end of the course, you’ll get up-and-running via Machine Learning with Python’s insightful projects to perform various Machine Learning tasks in the real world.

About the Authors

Alexander T. Combs is an experienced data scientist, strategist, and developer with a background in financial data extraction, natural language processing and generation, and quantitative and statistical modeling. He is currently a full-time lead instructor for a data science immersive program in New York City.

Prateek Joshiis an Artificial Intelligence researcher, the published author of five books, and a TEDx speaker. He is the founder of Pluto AI, a venture-funded Silicon Valley startup building an analytics platform for smart water management powered by deep learning. His work in this field has led to patents, tech demos, and research papers at major IEEE conferences. He has been an invited speaker at technology and entrepreneurship conferences including TEDx, AT&T Foundry, Silicon Valley Deep Learning, and Open Silicon Valley. Prateek has also been featured as a guest author in prominent tech magazines. His tech blog has received more than 1.2 million page views from over 200 countries and has over 6,600+ followers. He frequently writes on topics such as Artificial Intelligence, Python programming, and abstract mathematics. He is an avid coder and has won many hackathons utilizing a wide variety of technologies. He graduated from University of Southern California with a Master’s degree, specializing in Artificial Intelligence. He has worked at companies such as Nvidia and Microsoft Research.

Colibriis a technology consultancy company founded in 2015 by James Cross and Ingrid Funie. The company works to help its clients navigate the rapidly changing and complex world of emerging technologies, with deep expertise in areas like big data, data science, Machine Learning, and Cloud Computing. Over the past few years, they have worked with some of the world’s largest and most prestigious companies, including a tier 1 investment bank, a leading management consultancy group, and one of the world’s most popular soft drinks companies, helping all of them to better make sense of their data, and process it in more intelligent ways. The company lives by its motto: Data -> Intelligence -> Action.

Rudy Laiis the founder of Quant Copy, a sales acceleration startup using AI to write sales emails to prospects. By taking in leads from your pipelines, Quant Copy researches them online and generates sales emails from that data. It also has a suite of email automation tools to schedule, send, and track email performance—key analytics that all feed back into how our AI generated content. Prior to founding Quant Copy, Rudy ran HighDimension.IO, a machine learning consultancy, where he experienced firsthand the frustrations of outbound sales and prospecting. As a founding partner, he helped startups and enterprises with HighDimension.IO’s Machine-Learning-as-a-Service, allowing them to scale up data expertise in the blink of an eye.

In the first part of his career, Rudy spent 5+ years in quantitative trading at leading investment banks such as Morgan Stanley. This valuable experience allowed him to witness the power of data, but also the pitfalls of automation using data science and machine learning. Quantitative trading was also a great platform from which to learn a lot about reinforcement learning and supervised learning topics in a commercial setting. Rudy holds a Computer Science degree from Imperial College London, where he was part of the Dean’s List, and received awards such as the Deutsche Bank Artificial Intelligence prize.

Course Curriculum

Chapter 1: Python Machine Learning Projects

Lecture 1: The Course Overview

Lecture 2: Sourcing Airfare Pricing Data

Lecture 3: Retrieving the Fare Data with Advanced Web Scraping Techniques

Lecture 4: Parsing the DOM to Extract Pricing Data

Lecture 5: Sending Real-Time Alerts Using IFTTT

Lecture 6: Putting It All Together

Lecture 7: The IPO Market

Lecture 8: Feature Engineering

Lecture 9: Binary Classification

Lecture 10: Feature Importance

Lecture 11: Creating a Supervised Training Set with the Pocket App

Lecture 12: Using the embed.ly API to Download Story Bodies

Lecture 13: Natural Language Processing Basics

Lecture 14: Support Vector Machines

Lecture 15: IFTTT Integration with Feeds, Google Sheets, and E-mail

Lecture 16: Setting Up Your Daily Personal Newsletter

Lecture 17: What Does Research Tell Us about the Stock Market?

Lecture 18: Developing a Trading Strategy

Lecture 19: Building a Model and Evaluating Its Performance

Lecture 20: Modeling with Dynamic Time Warping

Lecture 21: Machine Learning on Images

Lecture 22: Working with Images

Lecture 23: Finding Similar Images

Lecture 24: Building an Image Similarity Engine

Lecture 25: The Design of Chatbots

Lecture 26: Building a Chatbot

Chapter 2: Python Machine Learning Solutions

Lecture 1: The Course Overview

Lecture 2: Preprocessing Data Using Different Techniques

Lecture 3: Label Encoding

Lecture 4: Building a Linear Regressor

Lecture 5: Regression Accuracy and Model Persistence

Lecture 6: Building a Ridge Regressor

Lecture 7: Building a Polynomial Regressor

Lecture 8: Estimating housing prices

Lecture 9: Computing relative importance of features

Lecture 10: Estimating bicycle demand distribution

Lecture 11: Building a Simple Classifier

Lecture 12: Building a Logistic Regression Classifier

Lecture 13: Building a Naive Bayes’ Classifier

Lecture 14: Splitting the Dataset for Training and Testing

Lecture 15: Evaluating the Accuracy Using Cross-Validation

Lecture 16: Visualizing the Confusion Matrix and Extracting the Performance Report

Lecture 17: Evaluating Cars based on Their Characteristics

Lecture 18: Extracting Validation Curves

Lecture 19: Extracting Learning Curves

Lecture 20: Extracting the Income Bracket

Lecture 21: Building a Linear Classifier Using Support Vector Machine

Lecture 22: Building Nonlinear Classifier Using SVMs

Lecture 23: Tackling Class Imbalance

Lecture 24: Extracting Confidence Measurements

Lecture 25: Finding Optimal Hyper-Parameters

Lecture 26: Building an Event Predictor

Lecture 27: Estimating Traffic

Lecture 28: Clustering Data Using the k-means Algorithm

Lecture 29: Compressing an Image Using Vector Quantization

Lecture 30: Building a Mean Shift Clustering

Lecture 31: Grouping Data Using Agglomerative Clustering

Lecture 32: Evaluating the Performance of Clustering Algorithms

Lecture 33: Automatically Estimating the Number of Clusters Using DBSCAN

Lecture 34: Finding Patterns in Stock Market Data

Lecture 35: Building a Customer Segmentation Model

Lecture 36: Building Function Composition for Data Processing

Lecture 37: Building Machine Learning Pipelines

Lecture 38: Finding the Nearest Neighbors

Lecture 39: Constructing a k-nearest Neighbors Classifier

Lecture 40: Constructing a k-nearest Neighbors Regressor

Lecture 41: Computing the Euclidean Distance Score

Lecture 42: Computing the Pearson Correlation Score

Lecture 43: Finding Similar Users in a Dataset

Lecture 44: Generating Movie Recommendations

Lecture 45: Preprocessing Data Using Tokenization

Lecture 46: Stemming Text Data

Lecture 47: Converting Text to Its Base Form Using Lemmatization

Lecture 48: Dividing Text Using Chunking

Lecture 49: Building a Bag-of-Words Model

Lecture 50: Building a Text Classifier

Lecture 51: Identifying the Gender

Lecture 52: Analyzing the Sentiment of a Sentence

Lecture 53: Identifying Patterns in Text Using Topic Modelling

Lecture 54: Reading and Plotting Audio Data

Lecture 55: Transforming Audio Signals into the Frequency Domain

Lecture 56: Generating Audio Signals with Custom Parameters

Lecture 57: Synthesizing Music

Lecture 58: Extracting Frequency Domain Features

Lecture 59: Building Hidden Markov Models

Lecture 60: Building a Speech Recognizer

Lecture 61: Transforming Data into the Time Series Format

Lecture 62: Slicing Time Series Data

Lecture 63: Operating on Time Series Data

Lecture 64: Extracting Statistics from Time Series

Lecture 65: Building Hidden Markov Models for Sequential Data

Lecture 66: Building Conditional Random Fields for Sequential Text Data

Lecture 67: Analyzing Stock Market Data with Hidden Markov Models

Lecture 68: Operating on Images Using OpenCV-Python

Lecture 69: Detecting Edges

Lecture 70: Histogram Equalization

Lecture 71: Detecting Corners and SIFT Feature Points

Lecture 72: Building a Star Feature Detector

Instructors

Packt Publishing
Tech Knowledge in Motion

Rating Distribution

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2 stars: 0 votes

3 stars: 1 votes

4 stars: 2 votes

5 stars: 1 votes

Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

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